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Systems biology PDF

418 Pages·2017·7.942 MB·English
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Editedby JensNielsenand StefanHohmann SystemsBiology RelatedTitles Meyers,R.A.(ed.) Wittmann,Ch.,Liao,J.C.(eds.) SystemsBiology IndustrialBiotechnology Microorganisms 2012 (2Volumes) PrintISBN:978-3-527-32607-5 2017 Dehmer,M.,Emmert-Streib,F.,Graber,A., PrintISBN:978-3-527-34179-5 Salvador,A.(eds.) Wittmann,Ch.,Liao,J.C.(eds.) AppliedStatisticsforNetwork Biology IndustrialBiotechnology MethodsinSystemsBiology ProductsandProcesses 2011 2017 PrintISBN:978-3-527-32750-8 PrintISBN:978-3-527-34181-8 FurtherVolumesofthe Comingsoon: “AdvanvedBiotechnology” Series: Yoshida,T.(ed.) AppliedBioengineering Published: 2017 Villadsen,J.(ed.) PrintISBN:978-3-527-34075-0 FundamentalBioengineering Chang,H.N.(ed.) 2016 EmergingAreasin PrintISBN:978-3-527-33674-6 Bioengineering Love,J.Ch.(ed.) 2017 Micro-andNanosystemsfor PrintISBN:9783527340880 Biotechnology 2016 PrintISBN:978-3-527-33281-6 EditedbyJensNielsenandStefanHohmann Systems Biology VolumeEditors AllbookspublishedbyWiley-VCHare carefullyproduced.Nevertheless,authors, JensNielsen editors,andpublisherdonotwarrantthe ChalmersUniversityofTechnology informationcontainedinthesebooks, DepartmentofBiologyandBiological includingthisbook,tobefreeoferrors. Engineering Readersareadvisedtokeepinmindthat Kemivägen10 statements,data,illustrations,procedural 41296Göteborg detailsorotheritemsmayinadvertently Sweden beinaccurate. LibraryofCongressCardNo.:appliedfor StefanHohmann ChalmersUniversityofTechnology DepartmentofBiologyandBiological BritishLibraryCataloguing-in-Publication Engineering Data Kemigården4,room3054A Acataloguerecordforthisbookisavail- 41296Göteborg ablefromtheBritishLibrary. Sweden Bibliographicinformationpublishedbythe SeriesEditors DeutscheNationalbibliothek TheDeutscheNationalbibliothek liststhispublicationintheDeutsche SangYupLee Nationalbibliografie;detailed KAIST bibliographicdataareavailableonthe 373-1;Guseong-Dong Internetat<http://dnb.d-nb.de>. 291Daehak-ro,Yuseong-gu 305-701Daejon SouthKorea ©2017Wiley-VCHVerlagGmbH&Co. KGaA,Boschstr.12,69469Weinheim, Germany JensNielsen ChalmersUniversityofTechnology DepartmentofBiologyandBiological Allrightsreserved(includingthoseof Engineering translationintootherlanguages).Nopart Kemivägen10 ofthisbookmaybereproducedinany 41296Göteborg form – byphotoprinting,microfilm,or Sweden anyothermeans – nortransmittedor translatedintoamachinelanguage withoutwrittenpermissionfromthe GregoryStephanopoulos publishers.Registerednames,trademarks, MassachusettsInstitutsofTechnology etc.usedinthisbook,evenwhennot DepartmentofChemicalEngineering specificallymarkedassuch,arenottobe 77MassachusettsAvenue consideredunprotectedbylaw. Cambridge,MA02139 USA PrintISBN:978-3-527-33558-9 ePDFISBN:978-3-527-69616-1 Cover ePubISBN:978-3-527-69617-8 Sphere–fotolia_©ket4up MobiISBN:978-3-527-69615-4 oBookISBN:978-3-527-69613-0 CoverDesign AdamDesign Typesetting SPiGlobal,Chennai,India PrintingandBinding Printedonacid-freepaper V Contents ListofContributors XV AbouttheSeriesEditors XXIII 1 IntegrativeAnalysisofOmicsData 1 TobiasÖsterlund,MarijaCvijovic,andErikKristiansson Summary 1 1.1 Introduction 1 1.2 OmicsDataandTheirMeasurementPlatforms 4 1.2.1 OmicsDataTypes 4 1.2.2 MeasurementPlatforms 5 1.3 DataProcessing:QualityAssessment,Quantification,Normalization, andStatisticalAnalysis 6 1.3.1 QualityAssessment 7 1.3.2 Quantification 9 1.3.3 Normalization 10 1.3.4 StatisticalAnalysis 11 1.4 DataIntegration:FromaListofGenestoBiologicalMeaning 12 1.4.1 DataResourcesforConstructingGeneSets 13 1.4.1.1 GeneOntologyTerms 13 1.4.1.2 KEGGandReactome 13 1.4.1.3 Genome-ScaleMetabolicReconstructions 14 1.4.2 GeneSetAnalysis 14 1.4.2.1 GeneSetOverenrichmentTests 16 1.4.2.2 Rank-BasedEnrichmentTests 16 1.4.3 NetworksandNetworkTopology 17 1.5 OutlookandPerspectives 18 References 19 2 13CFluxAnalysisinBiotechnologyandMedicine 25 YiErnCheah,ClintonM.Hasenour,andJameyD.Young 2.1 Introduction 25 2.1.1 WhyStudyMetabolicFluxes? 25 2.1.2 WhyareIsotopeTracersImportantforFluxAnalysis? 26 VI Contents 2.1.3 HowareFluxesDetermined? 28 2.2 TheoreticalFoundationsof13CMFA 29 2.2.1 ElementaryMetaboliteUnits(EMUs) 30 2.2.2 FluxUncertaintyAnalysis 31 2.2.3 OptimalDesignofIsotopeLabelingExperiments 32 2.2.4 IsotopicallyNonstationaryMFA(INST-MFA) 34 2.3 MetabolicFluxAnalysisinBiotechnology 36 2.3.1 13CMFAforHostCharacterization 36 2.3.2 13CMFAforPinpointingYieldLossesandFutileCycles 39 2.3.3 13CMFAforBottleneckIdentification 41 2.4 MetabolicFluxAnalysisinMedicine 42 2.4.1 LiverGlucoseandOxidativeMetabolism 43 2.4.2 CancerCellMetabolism 47 2.4.3 FuelOxidationandAnaplerosisintheHeart 48 2.4.4 MetabolisminOtherTissues:Pancreas,Brain,Muscle,Adipose,and ImmuneCells 49 2.5 EmergingChallengesfor13CMFA 50 2.5.1 TheoreticalandComputationalAdvances:MultipleTracers, Co-cultureMFA,DynamicMFA 50 2.5.2 Genome-Scale13CMFA 51 2.5.3 NewMeasurementStrategies 52 2.5.4 High-ThroughputMFA 53 2.5.5 ApplicationofMFAtoIndustrialBioprocesses 53 2.5.6 IntegratingMFAwithOmicsMeasurements 54 2.6 Conclusion 55 Acknowledgments 55 Disclosure 55 References 55 3 MetabolicModelingforDesignofCellFactories 71 MingyuanTian,PrashantKumar,SanjanT.P.Gupta,andJenniferL.Reed Summary 71 3.1 Introduction 71 3.2 BuildingandRefiningGenome-ScaleMetabolicModels 72 3.2.1 GenerateaDraftMetabolicNetwork(Step1) 74 3.2.2 ManuallyCuratetheDraftMetabolicNetwork(Step2) 75 3.2.3 DevelopaConstraint-BasedModel(Step3) 77 3.2.4 RevisetheMetabolicModelthroughReconciliationwith ExperimentalData(Step4) 79 3.2.5 PredictingtheEffectsofGeneticManipulations 81 3.3 StrainDesignAlgorithms 83 3.3.1 FundamentalsofBilevelOptimization 84 3.3.2 AlgorithmsInvolvingOnlyGene/ReactionDeletions 94 3.3.3 AlgorithmsInvolvingGeneAdditions 94 3.3.4 AlgorithmsInvolvingGeneOver/Underexpression 95 Contents VII 3.3.5 AlgorithmsInvolvingCofactorChanges 98 3.3.6 AlgorithmsInvolvingMultipleDesignCriteria 99 3.4 CaseStudies 100 3.4.1 StrainsProducingLactate 100 3.4.2 StrainsCo-utilizingSugars 100 3.4.3 StrainsProducing1,4-Butanediol 102 3.5 Conclusions 103 Acknowledgments 103 References 104 4 Genome-ScaleMetabolicModelingandInsilicoStrainDesignof Escherichiacoli 109 MeiyappanLakshmanan,Na-RaeLee,andDong-YupLee 4.1 Introduction 109 4.2 TheCOBRAApproach 110 4.3 HistoryofE.coliMetabolicModeling 111 4.3.1 Pre-genomic-eraModels 111 4.3.2 Genome-ScaleModels 112 4.4 InsilicoModel-BasedStrainDesignofE.coliCellFactories 115 4.4.1 GeneDeletions 127 4.4.2 GeneUp/Downregulations 127 4.4.3 GeneInsertions 128 4.4.4 CofactorEngineering 128 4.4.5 OtherApproaches 128 4.5 FutureDirectionsofModel-GuidedStrainDesigninE.coli 129 References 130 5 AcceleratingtheDrugDevelopmentPipelinewithGenome-Scale MetabolicNetworkReconstructions 139 BonnieV.Dougherty,ThomasJ.MoutinhoJr.,andJasonPapin Summary 139 5.1 Introduction 139 5.1.1 DrugDevelopmentPipeline 140 5.1.2 OverviewofGenome-ScaleMetabolicNetwork Reconstructions 140 5.1.3 AnalyticalToolsandMathematicalEvaluation 141 5.1.3.1 FluxBalanceAnalysis(FBA) 141 5.1.3.2 FluxVariabilityAnalysis(FVA) 142 5.2 MetabolicReconstructionsintheDrugDevelopmentPipeline 142 5.2.1 TargetIdentification 143 5.2.2 DrugSideEffects 145 5.3 Species-LevelMicrobialReconstructions 146 5.3.1 MicrobialReconstructionsintheAntibioticDevelopment Pipeline 146 5.3.1.1 ApplicationsintheDrugDevelopmentPipeline 146 VIII Contents 5.3.2 Metabolic-Reconstruction-FacilitatedRationalDrugTarget Identification 147 5.3.2.1 TargetingGenesEssentialforBiomassProduction 147 5.3.2.2 TargetingVirulenceFactors 147 5.3.2.3 Metabolite-centricTargeting 148 5.3.3 RepurposingandExpandingUtilityofAntibiotics 149 5.3.3.1 VirtualDrugScreensInformedbyMetabolicReconstructions 149 5.3.3.2 LimitingResistancewithDrugCombinations 149 5.3.3.3 ImprovingTreatmentOptionsbyIncreasingSensitivityto Antibiotics 150 5.3.4 ImprovingToxicityScreenswiththeHumanMetabolicNetwork Reconstruction 150 5.4 TheHumanReconstruction 151 5.4.1 ApproachesfortheHumanReconstruction 152 5.4.2 TargetIdentification 152 5.4.2.1 DrugTargetinginCancer 152 5.4.2.2 DrugTargetinginMetabolicDiseases 153 5.4.3 ToxicityandOtherSideEffects 154 5.5 CommunityModels 155 5.5.1 Host–PathogenCommunityModels 155 5.5.2 EukaryoticCommunityModels 156 5.6 PersonalizedMedicine 156 5.7 Conclusion 157 References 158 6 ComputationalModelingofMicrobialCommunities 163 SiuH.J.Chan,MargaretSimons,andCostasD.Maranas Summary 163 6.1 Introduction 163 6.1.1 MicrobialCommunities 163 6.1.2 ModelingMicrobialCommunities 165 6.1.3 ModelStructures 165 6.1.4 QuantitativeApproaches 166 6.2 EcologicalModels 168 6.2.1 GeneralizedPredator–PreyModel 169 6.2.2 EvolutionaryGameTheory 170 6.2.3 ModelsIncludingAdditionalDimensions 171 6.2.4 AdvantagesandDisadvantages 171 6.3 Genome-ScaleMetabolicModels 172 6.3.1 IntroductionandApplications 172 6.3.2 Genome-ScaleMetabolicModelingofMicrobialCommunities 174 6.3.3 SimulationofMicrobialCommunitiesAssumingSteadyState 175 6.3.3.1 PredictingInteractionsUsingFBA 175 6.3.3.2 IdentifyingMinimalMediabyMixedIntegerLinear Programming 176 Contents IX 6.3.3.3 ParetoOptimalityAnalysisbyFBA 176 6.3.3.4 ModelingChemostatCo-culture 177 6.3.3.5 CommunityFBAwithCommunityMassBalance 177 6.3.4 DynamicSimulationofMultispeciesModels 177 6.3.5 SpatialandTemporalModelingofCommunities 178 6.3.6 UsingBilevelOptimizationtoCaptureMultipleObjective Functions 179 6.3.6.1 OptCom 179 6.3.6.2 d-OptCom 181 6.3.6.3 CASINOToolbox 181 6.3.6.4 AdvantagesandDisadvantages 182 6.3.6.5 CurrentChallengesandFutureDirections 182 6.4 ConcludingRemarks 183 References 183 7 DrugTargetingoftheHumanMicrobiome 191 HuaLing,JeeL.Foo,GourvenduSaxena,SanjaySwarup, andMatthewW.Chang Summary 191 7.1 Introduction 191 7.2 TheHumanMicrobiome 192 7.3 AssociationoftheHumanMicrobiomewithHuman Diseases 194 7.3.1 Nasal–SinusDiseases 194 7.3.2 GutDiseases 194 7.3.3 CardiovascularDiseases 196 7.3.4 MetabolicDisorders 196 7.3.5 AutoimmuneDisorders 197 7.3.6 LungDiseases 197 7.3.7 SkinDiseases 197 7.4 DrugTargetingoftheHumanMicrobiome 198 7.4.1 Prebiotics 198 7.4.2 Probiotics 200 7.4.3 Antimicrobials 201 7.4.3.1 Antibiotics 201 7.4.3.2 AntimicrobialPeptides 202 7.4.4 SignalingInhibitors 202 7.4.5 Metabolites 203 7.4.5.1 Short-ChainFattyAcids 203 7.4.5.2 BileAcids 203 7.4.6 MetaboliteReceptorsandEnzymes 204 7.4.6.1 MetaboliteReceptors 204 7.4.6.2 MetabolicEnzymes 204 7.4.7 Microbiome-AidedDrugMetabolism 205 7.4.7.1 DrugDeliveryandRelease 205 X Contents 7.4.7.2 DrugToxicity 206 7.4.8 ImmuneModulators 206 7.4.9 SyntheticCommensalMicrobes 207 7.5 FuturePerspectives 207 7.6 ConcludingRemarks 208 Acknowledgments 208 References 209 8 TowardGenome-ScaleModelsofSignalTransduction Networks 215 UlrikeMünzner,TimoLubitz,EddaKlipp,andMarcusKrantz 8.1 Introduction 215 8.2 ThePotentialofNetworkReconstruction 219 8.3 InformationTransferNetworks 222 8.4 ApproachestoReconstructionofITNs 225 8.5 TherxnconApproachtoITNWR 230 8.6 TowardQuantitativeAnalysisandModelingofLarge ITNs 234 8.7 ConclusionandOutlook 236 Acknowledgments 236 Glossary 237 References 238 9 SystemsBiologyofAging 243 JohannesBorgqvist,RiccardoDainese,andMarijaCvijovic Summary 243 9.1 Introduction 243 9.2 TheBiologyofAging 245 9.3 TheMathematicsofAging 249 9.3.1 DatabasesDevotedtoAgingResearch 249 9.3.2 MathematicalModelinginAgingResearch 249 9.3.3 DistributionofDamagedProteinsduringCellDivision:A MathematicalPerspective 256 9.3.3.1 CellGrowth 256 9.3.3.2 CellDeath 257 9.3.3.3 CellDivision 257 9.4 FutureChallenges 260 ConflictofInterest 262 References 262 10 ModelingtheDynamicsoftheImmuneResponse 265 ElenaAbad,PabloVilloslada,andJordiGarcía-Ojalvo 10.1 Background 265 10.2 DynamicsofNF-κBSignaling 266 10.2.1 FunctionalRoleandRegulationofNF-κB 266

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